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Model: limajr/nbr-1b-base
Source: Original Platform

README.md

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+---
+language:
+- pt
+license: apache-2.0
+tags:
+- portuguese
+- brazilian
+- llama
+- causal-lm
+- text-generation
+pipeline_tag: text-generation
+---
+
+# NBR-1B Base
+
+## Model Description
+
+NBR-1B is a **1 billion parameter** language model trained from scratch specifically for **Brazilian Portuguese**.
+
+### Key Features
+
+- **Parameters**: ~1B (968M)
+- **Architecture**: LLaMA-style Transformer with GQA (Grouped Query Attention)
+- **Context Length**: 4,096 tokens
+- **Training Tokens**: 25.17B
+- **Final Loss**: 2.15
+- **Tokenizer**: Custom SentencePiece BPE (32K vocab)
+
+### Architecture Details
+
+| Parameter | Value |
+|-----------|-------|
+| Hidden Size | 2048 |
+| Layers | 24 |
+| Attention Heads | 16 |
+| KV Heads | 4 (GQA) |
+| Intermediate Size | 5504 |
+| Vocab Size | 32000 |
+
+### Training Data
+
+Curated Portuguese corpus (~25B tokens):
+
+- monoHPLT-PT (GigaVerbo filtered)
+- FineWeb-2 PT (filtered)  
+- BlogSet-BR (MinHash deduplicated)
+- LegalPT
+- Corpus Carolina
+
+### Training Configuration
+
+- **Optimizer**: AdamW
+- **Learning Rate**: 1e-4 with cosine decay
+- **Batch Size**: ~524K tokens/update
+- **Precision**: BFloat16
+- **Hardware**: NVIDIA H200 (143GB)
+- **Training Time**: ~130 hours
+
+### Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("limajr/nbr-1b-base")
+tokenizer = AutoTokenizer.from_pretrained("limajr/nbr-1b-base")
+
+text = "O Brasil e um pais"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0]))
+```
+
+## License
+
+Apache 2.0